6 papers · 1 filter
Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models
Fabian Raisch, Felix Koch, Zack Xuereb Conti +2
The widespread adoption of data-driven, energy-efficient model predictive control (MPC) in buildings remains hindered by substantial effort to collect data and train models for ind…
Transfer Learning for Neural Parameter Estimation applied to Building RC Models
Fabian Raisch, Timo Germann, J. Nathan Kutz +2
Parameter estimation for dynamical systems remains challenging due to non-convexity and sensitivity to initial parameter guesses. Recent deep learning approaches enable accurate an…
Robust and Interpretable Graph Neural Networks for Power Systems State Estimation
Arbel Yaniv, Kilian Golinski, Christoph Goebel
This study analyzes Graph Neural Networks (GNNs) for distribution system state estimation (DSSE) by employing an interpretable Graph Neural Additive Network (GNAN) and by utilizing…
Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts
Fabian Raisch, Max Langtry, Felix Koch +3
Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine…
Price Aware Power Split Control in Heterogeneous Battery Storage Systems
Sheng Yin, Vivek Teja Tanjavooru, Thomas Hamacher +2
This paper presents a unified framework for the optimal scheduling of battery dispatch and internal power allocation in Battery energy storage systems (BESS). This novel approach i…
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
Fabian Raisch, Thomas Krug, Christoph Goebel +1
Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging…